Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1815003(2022)

Indoor Scene Object Detection Based on Improved YOLOv4 Algorithm

Weigang Li*, Chao Yang, Lin Jiang, and Yuntao Zhao
Author Affiliations
  • Engineering Research Center for Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
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    References(27)

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    [9] Yao X Y. Research on indoor object detection based on deep learning[D](2018).

    [10] Li W G, Ye X, Zhao Y T et al. Strip steel surface defect detection based on improved YOLOv3 algorithm[J]. Acta Electronica Sinica, 48, 1284-1292(2020).

    [23] Zhang Y B, Guo W, Zhou Y et al. Real-time target detection of underwater relics based on multigranularity pruning[J]. Laser & Optoelectronics Progress, 58, 1410019(2021).

    [26] Yu C D, Bi X J, Han Y et al. Particle image velocimetry based on a lightweight deep learning model[J]. Acta Optica Sinica, 40, 0720001(2020).

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    Weigang Li, Chao Yang, Lin Jiang, Yuntao Zhao. Indoor Scene Object Detection Based on Improved YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815003

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    Paper Information

    Category: Machine Vision

    Received: Jun. 18, 2021

    Accepted: Jul. 20, 2021

    Published Online: Aug. 29, 2022

    The Author Email: Li Weigang (liweigang.luck@foxmail.com)

    DOI:10.3788/LOP202259.1815003

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